本研究では, 感情と音声の音響パラメータの関係について検討している. まず, 単母音 [え] を用いて, 音長, 音圧, ピッチ周期, スペクトル包絡線の傾斜の4つのパラメータを変化させ, 種々の刺激音を合成した. 次に, これらの刺激音を用いて, 指定した感情を表しているかどうか聞きとり実験を行い, 結果を Thurston のケースVを用いて尺度構成した. 対象とした感情は“驚き”,“喜び”,“恐れ”,“嫌悪”の4つの感情である. その結果, 各感情の聞きとりに効果を及ぼす音響パラメータがわかつた.

• Geometrically and materially non-linear finite element model using solid finite elements

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p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu–Zn–S thin films prepare...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy CONTROL-ORIENTED DESIGN OF SYSTEMS WITH OPERATING RANGES TO OPTIMIZE CLOSED-LOOP PERFORMANCE By Sanketh Bhat December 2010 Chair: Richard C. Lind Jr. Major: Aerospace Engineering Traditionally, the design of structural systems and vehicles has focused first on the design of the structure and then the control and other aspects are considered. But systems of the future will be evaluated based on closed-loop metrics and will be designed directly for ‘high-performance’ applications. Such capability depends on a synergistic integration of different disciplines and sub-systems, which cannot be captured by the traditional sequential design approach. Hence, it is imperative that a formulation for design be introduced that inherently considers the interactions between different disciplines and brings in controls earlier in the design process for optimal closed-loop performance. But existing approaches to the simultaneous design and optimization of the dynamics and the controller have some issues for realistic systems. This dissertation introduces an approach that overcomes some of the drawbacks of the existing simultaneous design approaches. This research introduces a formulation for multi-disciplinary design optimization with an emphasis on control. This approach notes that since mission capability is evaluated using a closed-loop configuration; the design must consider closed-loop performance when choosing system parameters. The approach does not actually optimize both the open-loop plant and controller simultaneously; rather, the approach minimizes a closed-loop norm by optimizing over the open-loop plant while constrained to satisfying conditions for controller existence. In this way, the approach finds the optimal plant

o elbow, entry and exits to pipes are presented. The results of increasing Q with increasing exponential values of hL regardless of D of pipe also presented. Therefore, a number of choices can be made between transporting process fluids using small D pipes (50>D>25 mm) and Reynolds (Re) numbers in the laminar region and large D pipes (100>D>50 mm) using Re numbers in turbulent regions. o elbows and entry and exit losses were correlated to the flow rate and D of pipe in order to establish the relationships which could be useful to design engineers and plant operators during design and plant operation. Decisions could be made early about the size and type of roughness of the pipe and appropriate Q of the fluid during design or plant operation based on the correlations presented in this paper. The challenge with the delivery of fluids is either non delivery or insufficient delivery to the desired destination. Oftentimes, it is either the insufficient pumping due to faulty pumps or high friction losses in the delivery system. This may be caused by pipe blockage or increased roughness which may contribute to high friction losses or the system has a high positive delivery head or insufficient net positive suction head at the pump. The purpose of the study was therefore to establish levels of friction losses in different sizes of pipes fitted with fittings; gate valve, 45 and 90 o elbows and exit and entry structures in order to determine the head losses that contribute to the increase of equivalent lengths of pipes that increase the delivery heads and hence problems of fluids delivery to desired destinations.

n this work an approach is investigated for development of a framework for the hierarchic reduction of the complexity of the optimization problem. It is based on combining the screening methods with metamodeling technique for measurement of design variables effects and recognition of their inter-dependencies. The methodology is applied over the entire range of response inputs that is opposed to a local sensitivity analysis. Nowadays, there is a variety of variance-based and screening methods to provide information on variables influence on the response. The easiest way to identify the impact of variables is by simply monitoring the regression coefficients of a linear or a quadratic polynomial model. A traditional statistical technique is ANOVA (ANalysis Of VAriance) which is mostly applied to identify linear effects but may be not appropriate for studying high non-linear main and interaction effects. Another popular strategies for variable screening are stepwise selection (Neter et al.), forward (Mantel) and backward (Freedman) methods, and Morris elementary effects screening method. The criticism of these “one-variable-in-time” strategies is that they do not handle the interaction between variables. As alternative methods that consider the interaction effects, the Iterated Fractional Factorial Design (Saltelli et al.) as a type of group screening and the Sobol’s variance-based approach related to Total Sensitivity Indices (TSI) are often used. More recently, an analytical variance-based Global Sensitivity Analysis (GSA) has been developed by Chen et al.. A main feature of the approach is a generalized analytical formulation for sensitivity indices through the use of well-know metamodels as Polynomial Response Surface (PRS), Kriging, Radial Basis Functions (RBF), and MARS. Principal Component Analysis (PCA) was proposed by Jolliffe. In the PCA, the number of main effects is much less than the dimension of the original design space, but they are not correlated with each other. Further development and applications of PCA in design optimization can be found for example in Ghisu et al. and Thakur et al.. Each screening method has its limitations, so the choice of which strategy to be used depends on several factors, for example on the number of design variables in the model and on the number of samples in the design of experiments. Other considerations may include the robustness of methods, conditions of implementations (e.g. how easy to use). For large-scale problems involving computationally expensive simulations, a priority is to use a relatively small number of samples. In the present study, a combination of two screening methods is used. The first method is a main effects estimation technique based on one-dimensional Moving Least Squares metamodel data analysis. This method is inexpensive and may be served as a basis for elimination of insignificant variables especially when the number of sampling points is relatively small in respect to the number of design variables. The second method is more sophisticated and involves a backward screening procedure together with n-dimensional Moving Least Squares metamodel based on cross-validation error estimation. As it will be demonstrated below, this approach is also able to identify the interaction effects. The outline of the paper is organised as follows. Section (II) presents a metamodel building technique including description of a Moving Least Squares Method based on cross-validation error estimation, weights definition, and design of experiments. Section (III) describes variables screening methods implemented in this work. The examples are presented in Sections (IV-V), followed by Conclusions.

ii Acknowledgements iii List of Symbols xiii

[This abstract is based on the author's abstract.] The application of design of experiment techniques for process optimization is examined. Emphasis is given to confounding effects when partial factorial designs are used. Four factors are examined..

[This abstract is based on the author's abstract.] A study is conducted at a firm engaged in manufacturing radio-frequency components (tuners), fly back transformers, and deflection yokes used in TVs. The study is related to the product "tune..

[1] We report results from the first 3 years (1999–2001) of long-term measurements of net ecosystem exchange (NEE) at an AmeriFlux site over a mixed hardwood forest in northern lower Michigan. The primary measurement methodology uses eddy covariance systems with closed-path infrared gas analyzers at two heights (46 and 34 m) above the forest (canopy height is ∼22 m). One objective is to contribute to a more firmly established methodology of estimating annual net ecosystem production (NEP), by systematically examining the consequences of several variations of criteria to identify periods of unreliable measurements, and to fill gaps in the data. We compared two methods to fill data gaps (about 30% of time in 1999) due to missing observations or rejected data after quality control; one using short-term ensemble averages of the daily course and the other by semiempirical parametric models based on relationships between ecosystem respiration and soil temperature and between gross ecosystem photosynthetic uptake and photosynthetically active radiation. The modeled estimates were also used to replace eddy covariance fluxes during periods of weak and/or poorly developed turbulence when eddy covariance measurements cannot be expected to represent the ecosystem exchange. Examination of the fractions of eddy covariance fluxes and storage change relative to the expected ecosystem respiration suggested a friction velocity (u*) of 0.35 m s−1 as the lower limit for the acceptance of micrometeorologically determined NEE for this site. The differences in estimated annual NEP due to different criteria of data acceptance, measurement height, or gap-filling strategies turned out to be at least as large as the interannual variations over the 3 years. After discussing various analysis strategies we conclude that the best estimate of annual NEP at our site is achieved by replacing data gaps and measurements in low-u* conditions at all times with site and period-specific parametric models, using the upper measurement level (about 2.1 canopy heights). These “best estimates” of annual NEP for 1999–2001 amounted to 170 (1999), 160 (2000), and 80 (2001) g C m−2. We also discuss some problems of assigning quantitative estimates of uncertainty for annual NEP.

[1] Research to compare the performance of precipitation-runoff models (and indeed models of other hydrologic and geophysical processes) is increasingly reported in the literature. A characteristic of almost all such intercomparisons at present is that each model is tested only by the research group (the “operator”) that has developed it, so that differences between models are confounded with differences between operators. It has long been recognized that each model should be tested using data from a range of watersheds, giving replication of results under different conditions of climate, vegetation, geology, and other factors. This paper argues that in addition, models must be tested by more than one operator and that the other necessary feature of good experimental practice (namely, the use of randomization procedures where models are to be tested in sequence) is also needed in model intercomparison studies. This removes possible bias, inadvertent or otherwise, and allows the uncertainties in estimates of model performance to be calculated. In the context of intercomparison experiments, the paper discusses a number of issues: (1) experimental design as a means of eliminating operator bias; (2) the comparability between models for which different numbers of parameters must be estimated; (3) the consequence for estimates of uncertainty (as measured by standard errors of mean measures of model performance) of assuming that the watersheds used in an intercomparison experiment are sampled from a wider population of watersheds; (4) the “representativity” of the hydrologic records used for calibration and validation of models; (5) whether the periods of record used in calibration/validation from different watersheds should be synchronized for each model run; and (6) procedures for estimating how well models would perform on ungaged basins.

[1] A Bayesian approach was used to fit a conceptual transpiration model to half-hourly transpiration rates for a sugar maple (Acer saccharum) stand collected over a 5-month period and probabilistically estimate its parameter and prediction uncertainties. The model used the Penman-Monteith equation with the Jarvis model for canopy conductance. This deterministic model was extended by adding a normally distributed error term. This extension enabled using Markov chain Monte Carlo simulations to sample the posterior parameter distributions. The residuals revealed approximate conformance to the assumption of normally distributed errors. However, minor systematic structures in the residuals at fine timescales suggested model changes that would potentially improve the modeling of transpiration. Results also indicated considerable uncertainties in the parameter and transpiration estimates. This simple methodology of uncertainty analysis would facilitate the deductive step during the development cycle of deterministic conceptual models by accounting for these uncertainties while drawing inferences from data.

Writers of statistics textbooks tend to copy other textbooks rather than draw on experience. This leaves a serious gap: Techniques that are highly useful in practice are not taught. With this series of columns I am trying to fill that gap. The first column [1] pointed out that doing something (imperfect) is better than doing nothing. The second [2] was about the value of transforming your data. A third neglected lesson about data analysis is plot your data. More precisely, make all reasonable graphs of your data. Make a histogram of every measurement (to see its distribution), plot every measurement against its date, and plot every measurement against every other measurement. This is a good way to generate ideas. To read almost any statistics textbook, even the best (e.g., Box et al. [3]), you'd think science was all about testing ideas. It isn't. Where do the tested ideas come from? Idea generation, which these books ignore, is just as important as idea testing. One of the best ways to generate new ideas worth testing, I have found, is to make many graphs of my data. It is like searching for buried treasure. New ideas worth testing are very valuable but hard to find. Only a tiny fraction (1%?) of the graphs I've made led to new ideas but some of those ideas had a big effect. My graphs generated new ideas in two ways. 1) Causal-ity. The graph suggested a cause– effect relation I hadn't thought of. 2) Simplicity. Something turned out to be simpler than expected. Here are examples. Causality Weight and sleep duration Hoping to sleep better, I measured my sleep duration [4]. During a routine analysis of the data, I plotted sleep duration versus date. The graph showed that my sleep duration had sharply decreased several months earlier, which I hadn't noticed. The sleep change occurred at exactly at the same time I'd lost weight by changing my diet. The dietary change was to eat less-processed food, food closer to its natural state—to eat oranges instead of orange juice, for example. The upper panel of Figure 1 shows the decrease in sleep duration; the lower panel of Figure 1 shows the weight loss. Before seeing these data, I'd never suspected that weight controls sleep duration, nor had anyone else, as far as I know. I later found other evidence for this [4]. In a circuitous way, the …

With the increasing popularity of Web 2.0 streams, people become overwhelmed by the available information. This is partly countered by tagging blog posts and tweets, so that users can filter messages according to their tags. However, this is insufficient for detecting newly emerging topics that are not reflected by a single tag but are rather expressed by unusual tag combinations. This paper presents enBlogue, an approach for automatically detecting such emergent topics. EnBlogue uses a time-sliding window to compute statistics about tags and tag-pairs. These statistics are then used to identify unusual shifts in correlations, most of the time caused by real-world events. We analyze the strength of these shifts and measure the degree of unpredictability they include, used to rank tag-pairs expressing emergent topics. Additionally, this "indicator of surprise" is carried over to subsequent time points, as user interests do not abruptly vanish from one moment to the other. To avoid monitoring all tag-pairs we can also select a subset of tags, e. g., the most popular or volatile of them, to be used as seed-tags for subsequent pair-wise correlation computations. The system is fully implemented and publicly available on the Web, processing live Twitter data. We present experimental studies based on real world datasets demonstrating both the prediction quality by means of a user study and the efficiency of enBlogue.

With the increasing emphasis on reduced time to market, increased product quality, and improved customer satisfaction, has come the need for more efficient and effective procedures to develop new and redesigned products. With this emphasis companies have realized that the design of products and manufacturing processes are significantly interconnected. Likewise, a deeper consideration of the problem reveals that, in fact, the efficient design of a product is dependent on all of the processes of the enterprise. Therefore, if an organization is to be effective in markets in the future they must develop integrated strategies for designing new products, their production processes, as well as the supporting processes, including manufacturing, sales, distribution, and product support. Given the complexity of this endeavor there is a need for systems modeling and simulation tools which can support all aspects of the design of products, processes, and enterprises.

Whilst human and animal experimentation receives much attention every year by the general media, the public and the scientific community, little has been done on how to reduce the number of living subjects to be submitted to experimental research. Researchers who design human and animal experiments are conscious that trials on such subjects present a problem, which is not only scientific, but also ethical. The national research and ethics committees regulate the selection and number of patients or mammals to be treated and make the justification of the number of living subjects an aspect of paramount importance in different scientific disciplines. However, a rational methodology to estimate a sensible number that is in accordance with the parameters of the Association of Research Ethics Committees has not been well explored. In this article, a simple statistical approach for the study of the confidence in a model is proposed. The approach can pave the way to a potentially useful method to estimate a sensible number of subjects which is in accordance with the general principles of ethical bodies from all over the world.

When evaluating the research output of scientists, institutions or journals, different portfolios of publications are usually compared with each other. e.g., a typical problem is to select, between two scientists of interest, the one with the most cited portfolio. The total number of received citations is a very popular indicator, generally obtained by bibliometric databases. However, databases are not free from errors, which may affect the result of evaluations and comparisons; among these errors, one of the most significant is that of omitted citations. This paper presents a methodology for the pair-wise comparison of publication portfolios, which takes into account the database quality regarding omitted citations. In particular, it is defined a test for establishing if a citation count is (or not) significantly higher than one other. A statistical model for estimating the type-I error related to this test is also developed.

When developing new products many different customer needs must be fulfilled. Often a few of the needs are prioritized (as emphasized in Quality Function Deployment) and most effort in the project is focused on these needs. However, even though there may be only four or five customer needs that are important, it is not easy to develop a product that complies with all of these. A well-known approach to this problem is to use Design of Experiments to find the settings of the system parameters. In a situation with several system characteristics, the analysis of the experiment is not as uncomplicated as in the case with just one system characteristic. This problem has been dealt with in the literature but not as much as one would expect. In this paper we present a methodology to deal with multi-characteristic system improvement, using active expert involvement. Our methodology, which is based on methods from four areas: Expert Groups, Design of Experiments, Conjoint Analysis and Optimization, is introduced using an example. Copyright © 2000 John Wiley & Sons, Ltd.

When designing a marketing campaign, marketers must identify the appropriate mix of offer elements to optimize their desired measure of campaign performance (such as response, conversion, sales, and profit). This is not usually a trivial task since the number of offer elements making up a typical marketing campaign can be quite large. The chosen mix of offer elements included in a campaign is usually based on a combination of business judgment and the results of experimental testing. In this paper, we present an application of multivariate testing from the field of digital marketing analytics. We demonstrate how to design a fractional factorial test with JMP® and how to predict the optimal combination of offer elements with the GLIMMIX procedure.